About this Course
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다음 전문 분야의 4개 강좌 중 3번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

고급 단계

This is an advanced course, intended for learners with a background in computer vision and deep learning.

완료하는 데 약 20시간 필요

권장: 6 weeks of study, 5-6 hours per week...

영어

자막: 영어

배울 내용

  • Check

    Work with the pinhole camera model, and perform intrinsic and extrinsic camera calibration

  • Check

    Detect, describe and match image features and design your own convolutional neural networks

  • Check

    Apply these methods to visual odometry, object detection and tracking

  • Check

    Apply semantic segmentation for drivable surface estimation

다음 전문 분야의 4개 강좌 중 3번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

고급 단계

This is an advanced course, intended for learners with a background in computer vision and deep learning.

완료하는 데 약 20시간 필요

권장: 6 weeks of study, 5-6 hours per week...

영어

자막: 영어

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 2시간 필요

Welcome to Course 3: Visual Perception for Self-Driving Cars

4개 동영상 (총 18분), 4 readings
4개의 동영상
Welcome to the course4m
Meet the Instructor, Steven Waslander5m
Meet the Instructor, Jonathan Kelly2m
4개의 읽기 자료
Course Prerequisites15m
How to Use Discussion Forums15m
How to Use Supplementary Readings in This Course15m
Recommended Textbooks15m
완료하는 데 7시간 필요

Module 1: Basics of 3D Computer Vision

6개 동영상 (총 43분), 4 readings, 2 quizzes
6개의 동영상
Lesson 1 Part 2: Camera Projective Geometry8m
Lesson 2: Camera Calibration7m
Lesson 3 Part 1: Visual Depth Perception - Stereopsis7m
Lesson 3 Part 2: Visual Depth Perception - Computing the Disparity5m
Lesson 4: Image Filtering7m
4개의 읽기 자료
Supplementary Reading: The Camera Sensor30m
Supplementary Reading: Camera Calibration15m
Supplementary Reading: Visual Depth Perception30m
Supplementary Reading: Image Filtering15m
1개 연습문제
Module 1 Graded Quiz30m
2
완료하는 데 7시간 필요

Module 2: Visual Features - Detection, Description and Matching

6개 동영상 (총 44분), 5 readings, 1 quiz
6개의 동영상
Lesson 2: Feature Descriptors6m
Lesson 3 Part 1: Feature Matching7m
Lesson 3 Part 2: Feature Matching: Handling Ambiguity in Matching5m
Lesson 4: Outlier Rejection8m
Lesson 5: Visual Odometry9m
5개의 읽기 자료
Supplementary Reading: Feature Detectors and Descriptors30m
Supplementary Reading: Feature Matching15m
Supplementary Reading: Feature Matching15m
Supplementary Reading: Outlier Rejection15m
Supplementary Reading: Visual Odometry10m
3
완료하는 데 3시간 필요

Module 3: Feedforward Neural Networks

6개 동영상 (총 58분), 6 readings, 1 quiz
6개의 동영상
Lesson 2: Output Layers and Loss Functions10m
Lesson 3: Neural Network Training with Gradient Descent10m
Lesson 4: Data Splits and Neural Network Performance Evaluation8m
Lesson 5: Neural Network Regularization9m
Lesson 6: Convolutional Neural Networks9m
6개의 읽기 자료
Supplementary Reading: Feed-Forward Neural Networks15m
Supplementary Reading: Output Layers and Loss Functions15m
Supplementary Reading: Neural Network Training with Gradient Descent15m
Supplementary Reading: Data Splits and Neural Network Performance Evaluation10m
Supplementary Reading: Neural Network Regularization15m
Supplementary Reading: Convolutional Neural Networks10m
1개 연습문제
Feed-Forward Neural Networks30m
4
완료하는 데 3시간 필요

Module 4: 2D Object Detection

4개 동영상 (총 52분), 4 readings, 1 quiz
4개의 동영상
Lesson 2: 2D Object detection with Convolutional Neural Networks11m
Lesson 3: Training vs. Inference11m
Lesson 4: Using 2D Object Detectors for Self-Driving Cars14m
4개의 읽기 자료
Supplementary Reading: The Object Detection Problem15m
Supplementary Reading: 2D Object detection with Convolutional Neural Networks30m
Supplementary Reading: Training vs. Inference45m
Supplementary Reading: Using 2D Object Detectors for Self-Driving Cars30m
1개 연습문제
Object Detection For Self-Driving Cars30m
4.6
14개의 리뷰Chevron Right

Visual Perception for Self-Driving Cars의 최상위 리뷰

대학: RGOct 7th 2019

Many thanks for this amazing course!!!! was very hard to me but I have learned a lot!!! Thanks!!!

대학: AAJul 18th 2019

Content is great but lack of instructor support makes the course hard to understand.

강사

Avatar

Steven Waslander

Associate Professor
Aerospace Studies

토론토 대학교 정보

Established in 1827, the University of Toronto is one of the world’s leading universities, renowned for its excellence in teaching, research, innovation and entrepreneurship, as well as its impact on economic prosperity and social well-being around the globe. ...

자율 주행 자동차 전문 분야 정보

Be at the forefront of the autonomous driving industry. With market researchers predicting a $42-billion market and more than 20 million self-driving cars on the road by 2025, the next big job boom is right around the corner. This Specialization gives you a comprehensive understanding of state-of-the-art engineering practices used in the self-driving car industry. You'll get to interact with real data sets from an autonomous vehicle (AV)―all through hands-on projects using the open source simulator CARLA. Throughout your courses, you’ll hear from industry experts who work at companies like Oxbotica and Zoox as they share insights about autonomous technology and how that is powering job growth within the field. You’ll learn from a highly realistic driving environment that features 3D pedestrian modelling and environmental conditions. When you complete the Specialization successfully, you’ll be able to build your own self-driving software stack and be ready to apply for jobs in the autonomous vehicle industry. It is recommended that you have some background in linear algebra, probability, statistics, calculus, physics, control theory, and Python programming. You will need these specifications in order to effectively run the CARLA simulator: Windows 7 64-bit (or later) or Ubuntu 16.04 (or later), Quad-core Intel or AMD processor (2.5 GHz or faster), NVIDIA GeForce 470 GTX or AMD Radeon 6870 HD series card or higher, 8 GB RAM, and OpenGL 3 or greater (for Linux computers)....
자율 주행 자동차

자주 묻는 질문

  • 강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.

  • 강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

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